Abstract
Background and purpose: Deformable image registration (DIR) is a crucial tool in radiotherapy for extracting and modelling organ motion. However, when significant changes and sliding boundaries are present, it faces compromised accuracy and uncertainty, determining the subsequential contour propagation and dose accumulation procedures. Materials and methods: We propose an implicit neural representation (INR)-based approach modelling motion continuously in both space and time, named Continues-sPatial-Temporal DIR (CPT-DIR). This method uses a multilayer perception (MLP) network to map 3D coordinate (x,y,z) to its corresponding velocity vector (vx,vy,vz). The displacement vectors (dx,dy,dz) are then calculated by integrating velocity vectors over time. The MLP's parameters can rapidly adapt to new cases without pre-training, enhancing optimisation. The DIR's performance was tested on the DIR-Lab dataset of 10 lung 4DCT cases, using metrics of landmark accuracy (TRE), contour conformity (Dice) and image similarity (MAE). Results: The proposed CPT-DIR can reduce landmark TRE from 2.79mm to 0.99mm, outperforming B-splines' results for all cases. The MAE of the whole-body region improves from 35.46HU to 28.99HU. Furthermore, CPT-DIR surpasses B-splines for accuracy in the sliding boundary region, lowering MAE and increasing Dice coefficients for the ribcage from 65.65HU and 90.41% to 42.04HU and 90.56%, versus 75.40HU and 89.30% without registration. Meanwhile, CPT-DIR offers significant speed advantages, completing in under 15 seconds compared to a few minutes with the conventional B-splines method. Conclusion: Leveraging the continuous representations, the CPT-DIR method significantly enhances registration accuracy, automation and speed, outperforming traditional B-splines in landmark and contour precision, particularly in the challenging areas.
Abstract (translated)
背景和目的：曲面图像配准（DIR）在放射治疗中是提取和建模器官运动的关键工具。然而，当存在显著的变化和滑动边界时，它面临精度和不确定性的妥协，从而确定后续轮廓传播和剂量积累过程。材料和方法：我们提出了一种基于隐式神经表示（INR）的方法，在空间和时间上建模连续运动，名为继续-空间-时间曲面DIR（CPT-DIR）。该方法使用多层感知（MLP）网络将3D坐标（x，y，z）映射到其相应的速度向量（vx，vy，vz）。然后通过积分速度向量计算位移向量（dx，dy，dz）。MLP的参数可以快速适应新的病例，无需预训练，提高优化。DIR的性能在10个肺4DCT数据集上进行了测试，使用地标准确性（TRE）、轮廓一致性（Dice）和图像相似性（MAE）等指标。结果：与预训练的B-splines方法相比，CPT-DIR可以降低地标TRE从2.79mm降低到0.99mm，在所有病例中优于B-splines。整个身体的MAE从35.46HU降低到28.99HU。此外，CPT-DIR在滑动边界区域的精度超过了B-splines，降低了MAE并增加了脊椎的Dice系数从65.65HU和90.41%降低到42.04HU和90.56%，与75.40HU和89.30%没有配准相比。同时，CPT-DIR具有显著的速
URL
https://arxiv.org/abs/2405.00430
